Intelligent Intrusion Detection and Prevention Systems: The Role of AI and Machine Learning

Abstract

The growing complexity and frequency of cyberattacks have demonstrated the weaknesses of conventional IDPS, which are highly dependent on fixed rules and signature-based approaches. Artificial Intelligence (AI) has become a transformative solution to overcome these difficulties, allowing intrusion detection to be more adaptive, scalable and intelligent. This research paper discusses AI-driven IDPS through the lens of ML, DL, and RL models to detect and prevent network and host-based threats. The paper presents major datasets, evaluation metrics, and instruments that are typically used in research, as well as major limitations including data imbalance, adversarial attacks, model interpretability, and real-time processing constraints. Moreover, the paper talks about integration into modern security ecosystems, such as cloud, IoT, and edge environment. The future directions are described, with the focus on such developments as autonomous self-learning systems, federated learning, LLM, and edge-native detection mechanisms. The results show that AI-based IDPS provide a great opportunity to improve the resilience of cybersecurity, but additional studies are needed to eliminate current constraints and implement powerful, reliable, and scalable solutions.